Instructions to use mlx-community/granite-20b-code-base-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/granite-20b-code-base-8bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlx-community/granite-20b-code-base-8bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/granite-20b-code-base-8bit")
model = AutoModelForCausalLM.from_pretrained("mlx-community/granite-20b-code-base-8bit", device_map="auto")

MLX

How to use mlx-community/granite-20b-code-base-8bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/granite-20b-code-base-8bit")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use mlx-community/granite-20b-code-base-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/granite-20b-code-base-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/granite-20b-code-base-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlx-community/granite-20b-code-base-8bit

SGLang

How to use mlx-community/granite-20b-code-base-8bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlx-community/granite-20b-code-base-8bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/granite-20b-code-base-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlx-community/granite-20b-code-base-8bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/granite-20b-code-base-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use mlx-community/granite-20b-code-base-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "mlx-community/granite-20b-code-base-8bit" --prompt "Once upon a time"

Docker Model Runner
How to use mlx-community/granite-20b-code-base-8bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/granite-20b-code-base-8bit
```

granite-20b-code-base-8bit

File size: 783 Bytes

98ccd9f

{
    "activation_function": "gelu_pytorch_tanh",
    "architectures": [
        "GPTBigCodeForCausalLM"
    ],
    "attention_softmax_in_fp32": true,
    "attn_pdrop": 0.1,
    "bos_token_id": 0,
    "embd_pdrop": 0.1,
    "eos_token_id": 0,
    "initializer_range": 0.02,
    "layer_norm_epsilon": 1e-05,
    "model_type": "gpt_bigcode",
    "multi_query": true,
    "n_embd": 6144,
    "n_head": 48,
    "n_inner": 24576,
    "n_layer": 52,
    "n_positions": 8192,
    "pad_token_id": 0,
    "quantization": {
        "group_size": 64,
        "bits": 8
    },
    "resid_pdrop": 0.1,
    "scale_attention_softmax_in_fp32": true,
    "scale_attn_weights": true,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.38.1",
    "use_cache": true,
    "vocab_size": 49152
}